Abstract
While deep convolutional neural networks (CNNs) have
achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main
reason is that their learned models are easy to overfit on
the simplified AWGN model which deviates severely from
the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we
suggest training a convolutional blind denoising network
(CBDNet) with more realistic noise model and real-world
noisy-clean image pairs. On the one hand, both signaldependent noise and in-camera signal processing pipeline
is considered to synthesize realistic noisy images. On the
other hand, real-world noisy photographs and their nearly
noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork
with asymmetric learning to suppress under-estimation of
noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy photographs clearly demonstrate the superior performance of
CBDNet over state-of-the-arts in terms of quantitative metrics and visual quality. The code has been made available
at https://github.com/GuoShi28/CBDNet.